Automated quantification of digital radiographic image  quality

ABSTRACT

A method for determining image quality of a digital radiographic image. The method is executed at least in part by a computer system. The method obtains image data for the digital radiographic image, identifies one or more regions of interest in the digital radiographic image, derives an image quality score that indicates the image quality of the digital radiographic image by computing at least a contrast-to-noise value for image data content within the one or more regions of interest, and reports the derived image quality score for the image.

CROSS REFERENCE TO RELATED APPLICATIONS

Priority is claimed from co-pending U.S. Ser. No. 61/103,338,provisionally filed on Oct. 7, 2008, entitled METHOD FOR AUTOMATICQUANTIFICATION OF DIGITAL RADIOGRAPHIC IMAGE QUALITY to Wang et al.

Priority is claimed from U.S. Ser. No. 12/485,072 filed on Jun. 16, 2009entitled DIAGNOSTIC IMAGE PROCESSING WITH AUTOMATIC SELF IMAGE QUALITYVALIDATION to Luo et al.

FIELD OF THE INVENTION

The present invention relates generally to digital radiographic imagingand more particularly to methods for measuring and reporting the imagequality of digital radiographic images.

BACKGROUND OF THE INVENTION

Different types of digital radiography, encompassing both storagephosphor-based computed radiography and flat-panel detector-based directradiography, have been accepted in medical circles as replacements forconventional screen-film radiography and it is acknowledged that theyoffer improved workflow efficiency and improved capability for overallimage quality.

In a digital radiography system, the radiation exposure energy that iscaptured on radiation sensitive material is converted, pixel by pixel,to electronic digital image data which is then stored in memorycircuitry for subsequent processing and display on suitable electronicimage display devices. One driving force in the success of digitalradiography is the ability to visualize and communicate stored images,via data networks, to one or more remote locations for analysis anddiagnosis. This represents a workflow improvement over the handling andprocessing that is required for screen-film radiography, where exposedfilm must first be developed and checked, then packaged and delivered toa remote location for diagnosis.

Digital radiography systems have a variable speed and their performanceis mostly noise limited. These systems are relatively easier to use, aresomewhat more “forgiving” in terms of setup and exposure technique overfilm systems, and offer some inherent potential for image qualityimprovement. At the same time, however, the relative ease of operationand reduction of the need for rigorous attention to procedure can havedetrimental effects in practice. Ease of operability can tend to makex-ray technologists more relaxed when selecting exposure techniques andin positioning the patient when taking x-ray exams with digitalradiography systems. In some cases, this can effect image quality andultimately impact the radiologists' ability to make proper and timelydiagnosis. Thus, the need for Quality Assurance (QA) with digitalradiography systems is not reduced. In some cases, the need for QA withthe implementation of digital radiography may even be increased somewhatin order to generate x-ray images that have sufficient diagnostic valueto the radiologist or clinician.

The quality of a radiographic image can be quantified from a number ofaspects. These can include anatomy positioning, exposure coverage,motion, and anatomy contrast-to-noise (CNR) ratio, for example.

or a radiographic image to be considered diagnostically acceptable, thecontrast of the diagnostically relevant anatomical regions over thebackground noise level must exceed a threshold, so that the radiologistor clinician can overcome the effects of image noise and accuratelyperceive anatomical details. This suggests that there should be aproportional relationship between CNR and the diagnostic quality of theradiographic image. Thus, an image that exhibits high overall CNR levelsis more likely to be acceptable for diagnosis, whereas an image withmoderate to low CNR levels may have only borderline clinical value ormay even be unacceptable for diagnosis.

The problem of assessing CNR is made more difficult by the relativecomplexity of various types of radiographic image. Even within anyparticular radiographic image, CNR can vary depending upon the type oftissue that is imaged in a particular area of the image. CNR is thus afunction of both the image exposure level and the anatomical region, andalso a function of spatial frequency in the image, where both anatomicalfeatures and noise can be distributed differently.

The value of using CNR estimation for image correction and subsequentrendering, such as to enhance or reduce image contrast from raw imagedata, has been recognized, for example, refer to U.S. Pat. No. 7,321,674entitled “Method of Normalizing a Digital Signal Representation of anImage” (Vuylsteke '674). Vuylsteke '674 relates to rendering an imagebased on its overall CNR value, estimated from the histogram that bestcharacterizes the noise image (at the finest scale frequency band) andthe histogram that best characterizes image contrast (the fourth finestscale frequency band). In addition, CNR in this method is computedwithout consideration for how tissue characteristics vary over differentareas and at different spatial frequencies. As a result, computed datausing techniques such as described Vuylsteke '674 can be misleading,since the diagnostic information of interest may be in a portion of theimage having CNR at a different level than the overall CNR of the fullimage. Further, because this type of method calculates the contrast froma single frequency band (for example, the fourth finest scale frequencyband in the Vuylsteke '674 method), it does not capture the broadspatial frequency spectrum of the anatomical regions. An additionalproblem relates to how this CNR information is used. Once CNR iscomputed, an image having a poor CNR may still be processed using thesame rendering sequence as an image having acceptable CNR, perhaps withdifferent gain or contrast adjustment settings in an attempt tocompensate for low CNR. However, if CNR is below a certain minimumlevel, meaning that noise levels are excessive, no subsequent processingcan “rescue” the image content. As a result, using this conventionalapproach, an image that is of poor quality is simply processed anyway,and sent for radiologist or clinician viewing. This can negativelyimpact the diagnosis results and merely defers identification andsolution of the imaging problem.

Thus, it is seen that there would be benefits to a system that detectsimage quality problems earlier in the imaging workflow and makes itpossible to identify and correct at least some portion of such problemsmore quickly, allowing the exposures to be re-taken while the patient isstill present at the imaging site, for example.

SUMMARY OF THE INVENTION

It is an object of the present invention to advance the art ofdiagnostic imaging. With this object in mind, the present inventionprovides a method for determining image quality of a digitalradiographic image for a patient, the method executed at least in partby a computer system and comprising: obtaining image data for thedigital radiographic image; identifying one or more regions of interestin the digital radiographic image; deriving an image quality scoreindicative of the quality of the digital radiographic image by computingat least a contrast-to-noise value for image data content within the oneor more regions of interest; and reporting the derived image qualityscore for the image.

From another aspect, the present invention provides a method forevaluating a digital radiographic image, executed at least in part by acomputer system, comprising: obtaining the digital radiographic imagefor a patient; obtaining information about the condition of the patient;identifying one or more regions of interest in the digital radiographicimage according to the obtained information about the condition of thepatient; computing a contrast-to-noise value for image content withineach of the one or more identified regions of interest; combining thecomputed contrast-to-noise values according to a predetermined profilefor deriving a score of digital image quality; and reporting the derivedscore.

A feature of the present invention is that it computes CNR data for oneor more discrete portions of an image that have diagnostic relevance.

It is an advantage of the present invention that it allows atechnologist to learn about an imaging problem before the image is sentahead for further processing and viewing by a radiologist.

These objects are given only by way of illustrative example, and suchobjects may be exemplary of one or more embodiments of the invention.Other desirable objectives and advantages inherently achieved by thedisclosed invention may occur or become apparent to those skilled in theart. The invention is defined by the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of theinvention will be apparent from the following more particulardescription of the embodiments of the invention, as illustrated in theaccompanying drawings. The elements of the drawings are not necessarilyto scale relative to each other.

FIG. 1 shows a digital radiography image having multiple regions ofinterest (ROIs).

FIG. 2 is a logic flow diagram showing steps for using CNR computationin one embodiment of the present invention.

FIG. 3 is a logic flow diagram showing steps for using CNR computationfor a single ROI in an alternate embodiment of the present invention.

FIG. 4 is a block diagram showing operator interaction using theprocessing provided in one embodiment.

FIG. 5 is a plan view of a graphical user interface (GUI) for assigningweight values to an ROI in an image.

FIG. 6 is a plan view of a graphical user interface (GUI) for assigningweight values and identifying ROI characteristics in an image.

FIG. 7 is a schematic block diagram that shows a machine learning agentthat is trained to generate a diagnostic image quality score.

DETAILED DESCRIPTION OF THE INVENTION

The following is a detailed description of the preferred embodiments ofthe invention, reference being made to the drawings in which the samereference numerals identify the same elements of structure in each ofthe several figures.

The method of the present invention is executed, at least in part, by acomputer or similar logic control processor that executes programmedinstructions. The computer may include one or more storage media, forexample; magnetic storage media such as magnetic disk (such as a harddisk) or magnetic tape; optical storage media such as optical disk,optical tape, or machine readable bar code; solid-state electronicstorage devices such as random access memory (RAM), or read-only memory(ROM); or any other physical devices or media employed to store acomputer program having instructions for controlling one or morecomputers to practice the method according to the present invention.Additional storage hardware may be provided by archival storage systemsand devices.

As noted in the Background above, contrast-to-noise ratio (CNR) is oneparameter that can be used to characterize overall image quality indigital radiographic imaging applications. Even though CNR is known andhas been used for this purpose, however, this value, considered byitself, may not be an indicator of the actual diagnostic value of theimage in any particular case.

Embodiments of the present invention address this issue by evaluatingCNR as it relates to specific regions of interest (ROIs) in the digitalradiographic image (that is, based on the purpose of the exam) as wellas the spatial frequencies of each ROI. Referring to FIG. 1, forexample, a chest x-ray 10 can have a number of different ROIs. ROIs inthis example include a spinal region 12, lung field regions 14 and 15, ashoulder region 16, a heart region 17, and a diaphragm region 18.

Depending on the purpose of an x-ray exam, the requirements for CNRwithin each ROI can be very different. For example for thoracic exams,the viewing radiologist or clinician may be primarily interested in thelung field region of interest 14 and the heart ROI 17, and may have lessinterest in other parts of the x-ray, thus only the CNR of lung fieldROI 14 and heart ROI 17 is of significant diagnostic relevance for sucha case. In another example, for a rib exam, the radiologist or clinicianmay be primarily interested in rib bone structures instead of in thelung field, thus the CNR for the ROI containing the ribs becomes themost relevant and other areas are of less importance.

Using regional CNR as a measure of image quality can help the x-raytechnologist to ensure that proper x-ray exposure (normally, as low asis reasonably achievable) be applied for patient imaging in view of theintended purpose of the x-ray exam. CNR can also be used to guide thex-ray technologist to use an exposure technique that is best suited tothe purpose of a particular exam.

It can be appreciated that the example described with reference to FIG.1 can also apply for other types of images as well as for chest x-rays.That is, because different types of tissue are best imaged usingdifferent techniques, such as different exposure settings or with use ofa grid, for example, it can be advantageous to evaluate and report CNRmore specifically, as it relates to particular ROIs.

The logic flow diagram of FIG. 2 shows computer logic for a sequence forautomatic digital radiography image quality evaluation that measures anduses CNR information according to one or more ROIs in the image. In anobtain image step S100, a digital radiographic image is obtained from aDR or CR imaging apparatus. The image can be unprocessed “raw” dataobtained directly from the radiographic detector or CR scanner or may berendered data, generated after one or more processing steps, such asafter processing for image display, for example. In some embodiments,the image evaluated in this process is electronically stored image data,previously rendered. When unprocessed raw data is used, the CNRquantifies the relative quality of the originally captured image signalitself. For example, large-sized patient images that are captured withan anti-scatter grid have a higher image contrast, wider signal dynamicrange, and slightly higher noise level, but higher CNR as compared withthe same patient images captured under identical patient exposure levelswithout a grid. Using the CNR calculation and reporting of embodimentsof the present invention can help the x-ray technologist to improve theimage acquisition method by promoting the use of a grid whenappropriate. When CNR is calculated from the processed image, it canindicate quantitatively whether or not the presentation-ready image isof sufficient diagnostic quality. Further, when the method is applied tothe processed image, the CNR can also be used as feedback to the imageprocessing software algorithm for automatic adjustment of the imageprocessing parameters in order to achieve a predetermined CNR andpredetermined image quality.

An optional obtain metadata step S110 provides information about thetype of image and about its purpose relative to a particular patient.For example, a thoracic x-ray for one patient may be obtained forevaluation of lung fields; a thoracic x-ray for another patient may havebeen obtained in order to evaluate a rib or spine injury; a thoracicx-ray for a third patient may have been obtained for evaluating a heartcondition. This metadata can include information about the patient'scondition and can be helpful in subsequent processing for identifyingparticularly relevant ROIs for this image. At a minimum, some basicmetadata is necessary, such as identifying the type of x-ray: chestx-ray, anterior-posterior (AP), (posterior-anterior) PA, or lateralview, mammographic image, or abdominal or limb image, for example. Theimage metadata may be entered into a computer system by the technologistor may be automatically extracted from a database of patient informationsystems, such as radiology information system (RIS), or hospitalinformation system (HIS), or the like.

Continuing with the sequence of FIG. 2, an ROI identification step S120then uses various image analysis and processing techniques and usesinformation about the image and/or patient in order to perform thenecessary segmentation and other steps that identify structures in theimage that locate ROIs. Methods for computer-based ROI identificationwithin a radiographic image are well-known and include various types ofsegmentation, pattern identification, filtering, and other techniques.

As noted earlier, methods of the present invention evaluate CNR withineach of one or more ROIs. The results of these evaluations can then beused singly or can be grouped in order to determine the diagnosticefficacy of the image. In the sequence of FIG. 2, CNR is computed foreach ROI in a looping step S130 with the sub-steps shown. An imagecontrast measurement step S132 quantifies image contrast within the ROI.A noise level measurement step S134 provides a noise value. Acomputation step S136 then computes the CNR for the region of interestfrom these results. Looping step S130, with its sub-steps S132, S134,and S136, then repeats for each identified ROI in the image.

Once computation of the CNR for each region of interest is concluded, anoptional composite Digital Image Quality Score (DIQS) computation stepS140 is executed, obtaining a CNR value that is applicable to the fullimage. A results reporting step S150 then provides the results of CNRcomputation in some way, such as in the form of an alert to the operatoror feedback displayed to a viewer or reported as data to an imageprocessing system or provided as data for use by an administrativesystem or other networked computer. Results reporting step S150 mayprovide a summary CNR value for the complete image or, alternately, mayprovide individual CNR values that are associated with each ROI. A scorethat is derived for the image could also be provided to anotherprocessing system, or displayed, such as a Digital Image Quality Score(DIQS), computed as described in more detail subsequently.

In some cases, there may be only a single ROI in an image that isrelevant for a certain diagnostic purpose and the image sequence may beoptimized for obtaining the best image for that purpose. For example,only the lung fields in a chest x-ray may be of interest for aparticular patient. Techniques for optimizing this type of image, eventhose that might otherwise compromise image quality relative to otherROIs, may have been practiced by the technologist. Referring to thelogic flow diagram of FIG. 3, there is shown an alternate sequence forautomatic digital radiography image quality evaluation that measures anduses CNR information for only one ROI in the image. A number of thesteps shown for this sequence are the same as those described withreference to FIG. 2. The image is obtained in obtain image step S100.Obtain metadata step S110 provides the information needed to identifythe particular ROI that is relevant in an ROI identification step S 122.CNR is computed for each ROI in a computation step S138 with itssub-steps S132, S134, and S136, similar to those used in the sequence ofFIG. 2. Results reporting step S150 then provides the outcome of CNRcalculation that can be directly displayed or otherwise provided to thetechnologist or used as data for evaluating the technologist or forobtaining data about the image capture technique used, data aboutfacility performance, or information about other aspects of the imagecapture process.

In one embodiment of the present invention, the sequence shown in FIG. 2or 3 provides prompt reporting to the technologist of acceptable orunacceptable CNR levels in the image that has just been taken. In thisembodiment, shown in the block diagram of FIG. 4, a technologist 22 at acontrol console 24 initially enters instructions for imaging andprovides data such as metadata about the image type or purpose. As notedearlier, this data may be obtained in an automated manner from othersources. A patient 28 is exposed and the image is obtained by aradiographic detector 20 and provided to a processor 30. Processor 30accesses the image data and performs the evaluation logic describedearlier with respect to FIG. 2 or 3 and determines whether or not thex-ray image data that was obtained is acceptable for diagnosticpurposes. This information is sent to control console 24 and displayedfor the technologist to review. A cautionary message 32 is displayed toalert technologist 22 when image quality for the obtained image is poor.This gives technologist 22 the opportunity to correct any condition(such as patient positioning) or setup variables (such as exposurelevel, exposure technique, and various image processing parameters, forexample) and to retake the image to obtain an improved image.

An alternate embodiment provides a mechanism for monitoring imagequality within a department or for determining whether or not trainingof the x-ray technologists would be particularly useful. In thisembodiment, stored images, in rendered format, are accessed, such asfrom a PACS (Picture Archiving and Communications System) or otherarchival system. Images from only one department or from only aparticular technologist can be accessed and evaluated for image qualitybased on CNR computation. Since the approach used in this method isbased on ROIs, even using this ROI data in composite, combined in someway to compute the CNR of the complete image, it can provide accurateinformation on the efficiency of radiology departments and personnel.For such an embodiment, results reporting step S150 (FIGS. 2 and 3)includes generating and, optionally printing or displaying, one or morereports or listings that provide the desired image quality data indexedaccording to individual or department performance, according to thepatient, according to a unit of equipment, or according to a particularimaging practice such as the use of grids, for example. Because CNR iscomputed for individual ROIs, results reporting can focus on resultsobtained for particular types of imaging or conditions.

CNR Calculation

CNR expresses a ratio of contrast to noise. Noise in the digitalradiographic image content tends to be distributed within the higherspatial frequency range. On the other hand, the data content thatrelates to anatomy has a relatively broad range distribution of spatialfrequency, but with a higher distribution in the lower frequency range.Thus, the noise level can be more readily estimated from the highfrequency band(s) of the image.

There are many automated methods that can be used for noise estimation.One method is to calculate the weighted sum of the higher frequency binsof the 2D Fourier spectrum of the input image. The weighting factorincreases correspondingly with increases in spatial frequency. Onedrawback of this method is that the result is the estimate of theoverall noise level in the broad region over which the Fourier spectrumis calculated (such as within an ROI). Another method of estimating thenoise level is by calculating the standard deviation of pixel valueswithin a small region inside the high frequency band(s) of the inputimage. This method can provide a pixel-by-pixel noise estimation withineach frequency band of each ROI. There are known methods for decomposingthe input image into multi-frequency bands, from high to mid to lowfrequencies. Such methods can use various tools such as Laplacianfrequency decomposition, wavelet decomposition, or simple high-frequencyband-pass filtering. The resultant frequency bands can be either thesame size as the input image or of reduced size, depending on the methodbeing used, on the memory footprint, and on computation speedconsiderations. To improve the robustness of noise estimation, one canfirst calculate the noise in the highest frequency band, since the noisemagnitude most likely dominates the anatomy details in this band. Anestimate of the noise level in the other frequency bands can then bebased on electronically stored models established usinglaboratory-prepared flat field exposures. The overall noise level withina particular frequency band of a ROI can be calculated based on themean, median, or peak of the local noise standard deviation histogram.

There are a number of possible methods that can be used for contrastcalculation. In one embodiment, contrast is computed using the signaldynamic range from histogram data, compiled either from the entire imageor from one or more image ROIs. This method can be useful fordifferentiating images obtained with and without grids, for example. Thedynamic range of the histogram can be defined as the range of imagepixel values that lie between the minimum and maximum pixel values ofthe histogram. A more robust estimation can use the pixel valuedifference between lower (such as 1.0%) and higher (such as 99%)cumulative histogram percentiles.

Spatial frequency analysis also provides a mechanism for contrastcomputation. After the image is decomposed into a set of frequencybands, the anatomy edge signal content is mostly detectable in the low-to mid-high frequency bands. By calculating the regional pixel valuestandard deviation from those pixels with magnitude greater than theexpected noise level, one can estimate the corresponding anatomy edgemagnitude, based on the mean, median, or peak of the standard deviationvalues.

For images containing multiple diagnostic regions, such as for chestx-rays, the computed CNR must reasonably represent the image quality ofthese regions. For example, in a thoracic x-ray image, CNR is separatelycomputed from upper lung, mid-lung, lower lung, heart, diaphragm, andupper and lower mediastinum, and from rib and shoulder bone structures.

Automated evaluation of the CNR values can be performed by a trainedsystem, such as by a system using a neural network for learning how tomake distinctions according to its results from a base of expertassessments. To do this, a large number of samples are reviewed andscored by radiologists as a training set. Then, radiologist scoring isused as a corrective to adjust the scoring performed by the automatedsystem, to develop workable statistical models for an accurate way toevaluate images. In this manner, CNR values for different ROIs can begraded or scored, and the results used to help evaluate the relativequality of an image for these different ROIs.

A diagnostic image quality score (DIQS) of an image can be computed fromthe CNR of one or more ROIs in the image. In one embodiment, the DIQS ofthe image is obtained as an average of CNR values at each of the ROIs inthe image. With respect to the example given in FIG. 1, the CNR valuesfor regions 12, 14, 15, 16, 17, and 18 would be averaged. With such anaveraging, areas outside of the regions of interest are ignored for thepurpose of providing a measure of image quality.

More complex and adaptable arrangements for obtaining a DIQS value arealso envisioned. For example, an obtained CNR for an image or for an ROIwithin an image can be compared against a statistical distribution ofCNR values, thus providing a probability measurement of the relativequality of an image. A vector based on one or more CNR values for ROIswithin an image can also be calculated and used to determine how closelyan image comes to within an acceptable quality range. Other factors thatcan be relevant for determining the relative diagnostic value of animage can be related to clinical indications about the condition of thepatient or other patient metadata.

In another embodiment, DIQS can be calculated using information obtainedfrom a viewer entering operator commands. For example, a graphical userinterface (GUI), as shown in FIG. 5, allows the viewer to enter operatorcommands that define the shape, position, and size of an ROI for animage or for one or more types of images in one embodiment. A viewer canalso enter commands that assign weight values to different ROIs in animage, to skew the results of averaging or other operation that combinesmultiple ROIs, for example.

Referring to the GUI of FIG. 5, a benchmark image 38 appears on adisplay 40 for accepting viewer selection of ROIs and weighting input.Pull-down menus 42 and 44 enable viewer selection of overall type ofimage and ROI within the image. A control 46 and reporting window 48enable the viewer to assign and adjust a weight value to each specifiedROI type, displayed in benchmark image 38. In the GUI of FIG. 6, theviewer uses a mouse, touchscreen, or other pointing mechanism tomanually identify ROIs, tracing the outline of one or more irregularlyshaped ROIs 50 on display 40. Various controls 46 enable the viewer totrace the outline, to enter a weighting value, and to name and store thecombined data as a type of profile in this embodiment. This arrangementenables multiple profiles for DIQS calculation to be set up andelectronically stored, so that a particular weighting can be recalledfrom memory and applied to image data according to user preference. Adefault profile could be provided from a library of previously storedprofiles and used with any type of image, to allow editing of weightingvalues in an individual case, for example. Further administrativecommands (not shown) would provide capabilities for deleting or editingan existing set of profiles for DIQS calculation based on CNR and othervariables. A set of default profiles can be provided with the imagingsystem software, allowing users at a site to edit, delete, rename,generate, and assign profiles to individual images or to types of imagesas needed.

In another embodiment, information obtained about the patient can beused to determine how CNR values for each ROI are weighted. A clinicalindication of cancer in the patient record, for example, may be used toselect a particular weighting for tissues such as lung fields that mightbe of less interest for a patient who is imaged for a spine or ribstudy.

Referring to FIG. 7, a DIQS value can also be derived from learnedvalues, such as by using a machine learning agent 60. In this expertsystem embodiment of the present invention, machine learning agent 60,trained over a sequence of images with their different CNR values, canlearn to provide an output DIQS that closely matches an expert score ofan image. To accomplish this, the machine learning agent is firsttrained using a set of training images. The training step takes the CNRsof ROIs from different images, relevant patient metadata, and anexpert's score of the image as input. Expert systems for imageevaluation are well known to those skilled in the decision processingarts.

Expert systems training can use a neural network, a support vectormachine, or any other suitable machine learning methods to train machinelearning agent 60. The trained learning agent 60 takes the CNRs, shownin the example of FIG. 7 as CNR1, CNR2, . . . CNRn, of selected ROIs andpatient metadata to derive a DIQS that indicates the image quality foreach image. Here, DIQS can be a number obtained using expert scoring,probability factors, or a binary decision. In addition, DIQS isassociated with the patient's clinical information (stored in patientmetadata). Therefore, the DIQS value for a particular image can varydepending on the diagnostic purpose. For example, a chest PA image mayhave a blurred lung region but clear spine and rib bone structures. Sucha chest image would have a correspondingly high DIQS value for rib studyand a low DIQS value for lung study.

The CNR evaluation method of the present invention can be combined withother types of image quality measurements and assessments forradiological imaging. For example, CNR evaluation can be used inconjunction with computer-based methods that assess anatomy cutoff,inadvertent motion detection, and other imaging problems.

The invention has been described in detail with particular reference toa presently preferred embodiment, but it will be understood thatvariations and modifications can be effected within the spirit and scopeof the invention. The presently disclosed embodiments are thereforeconsidered in all respects to be illustrative and not restrictive. Thescope of the invention is indicated by the appended claims, and allchanges that come within the meaning and range of equivalents thereofare intended to be embraced therein.

PARTS LIST

-   10. Image-   12, 14, 15, 16, 17, 18. Region of interest (ROI)-   20. DR detector-   22. Technologist-   24. Console-   28. Patient-   30. Processor-   32. Message-   38. Benchmark image-   40. Display-   42, 44. Menu-   46. Control-   48. Window-   50. ROI-   60. Machine learning agent-   S100. Obtain image step-   S110. Obtain metadata step-   S120, S122. ROI identification step-   S130. Looping step-   S132. Image contrast measurement step-   S134. Noise level measurement step-   S136. Computation step-   S138. Computation step-   S140. DIQS computation step-   S150. Results reporting step-   CNR1, CNR2, CNRn. Contrast-to-noise ratio

1. A method for determining the image quality of a digital radiographicimage for a patient, the method executed at least in part by a computersystem and comprising: obtaining image data for the digital radiographicimage; identifying one or more regions of interest in the digitalradiographic image; deriving an image quality score indicative of thequality of the digital radiographic image by computing at least acontrast-to-noise value for image data content within the one or moreregions of interest; and reporting the derived image quality score forthe image.
 2. The method of claim 1 wherein identifying one or moreregions of interest comprises obtaining information about the medicalcondition of the patient.
 3. The method of claim 1 wherein reportingcomprises alerting a technologist to the derived image quality score. 4.The method of claim 1 wherein reporting comprises providing the derivedimage quality score to a networked computer system.
 5. The method ofclaim 1 wherein the digital radiographic image is in the form ofunprocessed data that is read from a digital radiographic detector orscanner.
 6. The method of claim 1 wherein the digital radiographic imageis a rendered image.
 7. The method of claim 1 wherein deriving the imagequality score further comprises averaging two or more contrast-to-noisevalues from within an image.
 8. The method of claim 1 wherein derivingthe image quality score further comprises obtaining a weighted averagefor two or more contrast-to-noise values from within the digitalradiographic image.
 9. The method of claim 1 wherein deriving the imagequality score further comprises comparing one or more obtainedcontrast-to-noise values from within an image against a statisticaldistribution of contrast-to-noise values.
 10. The method of claim 1wherein deriving the image quality score further comprises assigningweighting values to the one or more regions of interest.
 11. The methodof claim 1 wherein deriving the image quality score further comprisesusing an expert system.
 12. The method of claim 1 wherein deriving theimage quality score further comprises obtaining information on thecondition of a patient.
 13. The method of claim 1 wherein identifyingthe one or more regions of interest comprises obtaining viewerinstructions from a graphical user interface.
 14. A method forevaluating a digital radiographic image, executed at least in part by acomputer system, comprising: obtaining the digital radiographic imagefor a patient; obtaining information about the condition of the patient;identifying one or more regions of interest in the digital radiographicimage according to the obtained information about the condition of thepatient; computing a contrast-to-noise value for image content withineach of the one or more identified regions of interest; combining thecomputed contrast-to-noise values according to a predetermined profilefor deriving a score of digital image quality; and reporting the derivedscore.
 15. The method of claim 14 wherein reporting the score comprisesposting a message on a display.
 16. The method of claim 14 whereinreporting the score comprises providing the score to a subsequentprocess.
 17. The method of claim 14 wherein the digital radiographicimage is a rendered image.
 18. The method of claim 14 wherein combiningthe contrast-to-noise values comprises obtaining a weighted average ofthe values.
 19. The method of claim 14 wherein the predetermined profilecomprises one or more weighting factors for combining the computedcontrast-to-noise values.
 20. The method of claim 14 wherein combiningthe computed contrast-to-noise values further comprises accepting one ormore operator commands for setting up the profile.